Embodiments of the present invention relate to a concept for determining an orientation of a mobile device. Some embodiments of the present invention relate to a concept for Wi-Fi attitude and position tracking.
Modern smart phones are equipped with a variety of sensors. For positioning, satellite receivers, GSM (Global System for Mobile Communications) modules and wireless LAN (Local Area Network) modules can be used. Based on them, new and cheap approaches to pedestrian navigation can be provided. This enables new types of location based services for pedestrians ranging from calls for taxis, finding points of interests to city and museum guides.
Commonly the first choice for navigation is the Global Positioning System (GPS). However, the lack of precision and availability of GPS in urban and indoor environments is a prevalent problem. With the popular use of assisted GPS (A-GPS) in smart phones the startup time to the first GPS position fix and power consumption can be reduced. But, if signals are too week for detection, positioning fails. Moreover, outdoors the horizontal attitude of a device can be easily detected using electronic compasses. Nevertheless, indoors magnetic disturbances lead to unreliable compass outputs.
As an alternative or complementary solution for indoor environments Bahl et al. (Bahl, P., Padmanabhan, V.: Radar: an in-building rf-based user location and tracking system. In: Proceedings on INFOCOM the 19th Annual Joint Conference of the IEEE Computer and Communications Societies, Tel Aviv, Israel (2000)) suggested a positioning approach based on the received signal strength (RSS) in Wi-Fi™ (Wi-Fi: http://www.wi-fi.org/. Wi-Fi is a registered trademark of the Wi-Fi Alliance (2003)) networks. Nowadays, because of an increasing number of public and private access points, Wi-Fi positioning becomes more and more attractive for pedestrian navigation (Meyer, S., Vaupel, T., Haimerl, S.: Wi-fi coverage and propagation for localization purposes in permanently changing urban areas. In: Proceedings on IADIS the international Conference Wireless Applications and Computing, Amsterdam, The Netherlands (2008)) and is already integrated into many smart phones.
One remaining challenge in tracking pedestrians is estimating the heading of a person. Pedestrians move very slow and can turn anytime without changing their position. So, the speed vector of a pedestrian calculated from consecutive positions has a very low accuracy. The positioning accuracy can be improved by combining Wi-Fi positioning with dead reckoning, using low cost sensors as proposed in Seitz, J., Vaupel, T., Meyer, S., Gutierrez Boronat, J., Thielecke, J.: A hidden markov model for pedestrian navigation, in: Proceedings on WPNC the 7th Workshop on Positioning, Navigation and Communication, Dresden, Germany (2010); Seitz, J., Vaupel, T., Jahn, J., Meyer, S., Gutierrez Boronat, J., Thielecke, J.: A hidden markov model for urban navigation based on fingerprinting and pedestrian dead reckoning, in: Proceedings on the 13th International Conference on Information Fusion, Edinburgh, United Kingdom (2010). For pedestrians, dead reckoning can be improved by step detection, as analyzed in Jahn, J., Batzer, U., Seitz, J., Patiño Studencka, L., Gutierrez Boronat, J.: Comparison and evaluation of acceleration based step length estimators for handheld devices. In: Proceedings on the 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Zurich, Switzerland (2010). But estimating the attitude is still challenging.
Moreover, in the Article Wallbaum, M.: Indoor geolocation using wireless local area networks. Ph.D. thesis, Department of Computer Science, RWTH Aachen University (2005) 17. Welch, G., Bishop, G.: An introduction to the kalman filter. University of North Carolina at Chapel Hill, Chapel Hill, N.C., USA (1995) the specific problem of determining the position of objects and people within buildings is focused on. Thereby, a low-cost approach is based on wireless LANs, which are now widely deployed.
Wi-Fi positioning methods can be divided into two groups. The first group needs a database with the positions and the signal strength of known Wi-Fi access points, see e.g. in Skyhook Wireless: http://www.skyhookwireless.com and Schilit, B., LaMarca, A., Borriello, G., Griswold, W., McDonald, D., Lazowska, E., Balachandran, A., Hong, J., Iverson, V.: Challenge: Ubiquitous location-aware computing and the place lab initiative. In: Proceedings on the 1st ACM international workshop on Wireless mobile applications and services on WLAN hotspots, San Diego, Calif., USA (2003), and the second group needs a database of so called fingerprints, e.g. in Bahl, P., Padmanabhan, V.: Radar: an in-building rf-based user location and tracking system, in: Proceedings on INFOCOM the 19th Annual Joint Conference of the IEEE Computer and Communications Societies, Tel Aviv, Israel (2000); Castro, P., Chiu, P., Kremenek, T., Muntz, R.: A probabilistic room location service for wireless networked environments, in: Proceedings on UBICOMP the 3rd International Conference on ubiquitous computing, Atlanta, Ga., USA. Springer (2001); Haeberlen, A., Flannery, E., Ladd, A., Rudys, A., Wallach, D., Kavraki, L.: Practical robust localization over large-scale 802.11 wireless networks, in: Proceedings on MobiCom the 10th annual international conference on mobile computing and networking, Philadelphia, Pa., USA (2004); Ibach, P., Hbner, T., Schweigert, M.: Magicmap-kooperative positionsbestimmung ber wlan, in: Proceedings on the Chaos Communication Congress, Berlin, Germany (2004); Teuber, A., Eissfeller, B.: Wlan indoor positioning based on euclidean distances and fuzzy logic, In: Proceedings on WPNC the 3rd Workshop on Positioning, Navigation and Communication, Hannover, Germany (2006) and Youssef, M., Agrawala, A.: The horus location determination system, Wireless Networks 14(3), 357-374 (2008).
A fingerprinting database can be created by previously gathered RSS measurements. These are then referenced with the coordinates of the positions where they have been observed. Thus, one fingerprint contains a geo-referenced position, RSS values and the corresponding identifiers of the received access points. For positioning, fingerprinting is done by correlating current RSS measurements with the entries of the fingerprints in the database. Then, after selecting the best matching fingerprints, the user position can, for example, be calculated by a mean of the fingerprint positions weighted by their correlation results. More details on fingerprinting can be found in Bahl, P., Padmanabhan, V.: Radar: an in-building rf-based user location and tracking system, in: Proceedings on INFOCOM the 19th Annual Joint Conference of the IEEE Computer and Communications Societies, Tel Aviv, Israel (2000).
Each environment has a characteristical signal propagation. The RSS at a specific position depends on the path loss, shadowing by objects and multipath propagation. The higher the density of shadowing objects, the higher is the accuracy of Wi-Fi positioning, as different fingerprints are less similar in signal space. Therefore, indoors Wi-Fi positioning works very well because of the building structure and furniture. Outdoors, especially on large squares, the database correlation results in ambiguities.
To get meaningful Wi-Fi positioning results, in practice at least three access points are observed. An advantage of Wi-Fi positioning in urban environments is that the infrastructure is already set up. Existing private and public access points can be used. But on the other hand, positioning suffers from unobserved changes over time and the number of available access points varies from one place to another. An analysis of database changes can be found in Meyer, S., Vaupel, T., Haimerl, S.: Wi-fi coverage and propagation for localization purposes in permanently changing urban areas. In: Proceedings on IADIS the international Conference Wireless Applications and Computing, Amsterdam, The Netherlands (2008).
As reported in Meyer, S., Vaupel, T., Haimerl, S.: Wi-fi coverage and propagation for localization purposes in permanently changing urban areas, in: Proceedings on IADIS the international Conference Wireless Applications and Computing, Amsterdam, The Netherlands (2008) and Vaupel, T., Seitz, J., Kiefer, F., Haimerl, S., Thielecke, J.: Wi-fi positioning: System considerations and device calibration, in: Proceedings on the 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Zurich, Switzerland (2010), several methods are used to collect the measurements to build up the fingerprinting database.
FIG. 1 shows a representation of a geographic area indicating fingerprinting positions and the number of detected access points at each position. As a testbed for positioning, metropolitan areas of several major cities in Germany (including Berlin, Hamburg, Nuremberg and Munich) are used by the Fraunhofer IIS. In FIG. 1, a part of the database covering the city center of Nuremberg is presented. There, on average a fingerprint contains 21 access points, if there is coverage at all. In other words, FIG. 1 shows an example extracted from the Fraunhofer IIS Awiloc® fingerprinting database in Nuremberg, visualized on an openstreetmaps.org map. Thereby, dots indicate fingerprint positions and the amount of detected access points at each position, as depicted in the scale-up.
Wi-Fi positioning can be well used for localization in urban areas, because the density of receivable access points is high enough there. Especially indoors, Wi-Fi positioning offers reliable localization results, but a cheap and reliable attitude estimation system for indoor environments is missing. Ferromagnetic materials in building structures cause large magnetic disturbances that lead to unreliable compass headings. Inertial Navigation Systems (INS) based on micro electromechanical systems (MEMS) suffer from large drift errors with increasing time. This problem can be partially solved by sensor data fusion (Kraft, E.: A quaternion-based unscented kalman filter for orientation tracking. In: Proceedings on the 6th International Conference of Information Fusion, Cairns, Queensland, Australia (2003)).